{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fully-Connected Neural Nets\n",
    "In the previous homework you implemented a fully-connected two-layer neural network on CIFAR-10. The implementation was simple but not very modular since the loss and gradient were computed in a single monolithic function. This is manageable for a simple two-layer network, but would become impractical as we move to bigger models. Ideally we want to build networks using a more modular design so that we can implement different layer types in isolation and then snap them together into models with different architectures.\n",
    "\n",
    "In this exercise we will implement fully-connected networks using a more modular approach. For each layer we will implement a `forward` and a `backward` function. The `forward` function will receive inputs, weights, and other parameters and will return both an output and a `cache` object storing data needed for the backward pass, like this:\n",
    "\n",
    "```python\n",
    "def layer_forward(x, w):\n",
    "  \"\"\" Receive inputs x and weights w \"\"\"\n",
    "  # Do some computations ...\n",
    "  z = # ... some intermediate value\n",
    "  # Do some more computations ...\n",
    "  out = # the output\n",
    "   \n",
    "  cache = (x, w, z, out) # Values we need to compute gradients\n",
    "   \n",
    "  return out, cache\n",
    "```\n",
    "\n",
    "The backward pass will receive upstream derivatives and the `cache` object, and will return gradients with respect to the inputs and weights, like this:\n",
    "\n",
    "```python\n",
    "def layer_backward(dout, cache):\n",
    "  \"\"\"\n",
    "  Receive derivative of loss with respect to outputs and cache,\n",
    "  and compute derivative with respect to inputs.\n",
    "  \"\"\"\n",
    "  # Unpack cache values\n",
    "  x, w, z, out = cache\n",
    "  \n",
    "  # Use values in cache to compute derivatives\n",
    "  dx = # Derivative of loss with respect to x\n",
    "  dw = # Derivative of loss with respect to w\n",
    "  \n",
    "  return dx, dw\n",
    "```\n",
    "\n",
    "After implementing a bunch of layers this way, we will be able to easily combine them to build classifiers with different architectures.\n",
    "\n",
    "In addition to implementing fully-connected networks of arbitrary depth, we will also explore different update rules for optimization, and introduce Dropout as a regularizer and Batch Normalization as a tool to more efficiently optimize deep networks.\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "run the following from the cs231n directory and try again:\n",
      "python setup.py build_ext --inplace\n",
      "You may also need to restart your iPython kernel\n"
     ]
    }
   ],
   "source": [
    "# As usual, a bit of setup\n",
    "from __future__ import print_function\n",
    "import time\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from cs231n.classifiers.fc_net import *\n",
    "from cs231n.data_utils import get_CIFAR10_data\n",
    "from cs231n.gradient_check import eval_numerical_gradient, eval_numerical_gradient_array\n",
    "from cs231n.solver import Solver\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# for auto-reloading external modules\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "def rel_error(x, y):\n",
    "  \"\"\" returns relative error \"\"\"\n",
    "  return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('X_train: ', (49000, 3, 32, 32))\n",
      "('y_train: ', (49000,))\n",
      "('X_val: ', (1000, 3, 32, 32))\n",
      "('y_val: ', (1000,))\n",
      "('X_test: ', (1000, 3, 32, 32))\n",
      "('y_test: ', (1000,))\n"
     ]
    }
   ],
   "source": [
    "# Load the (preprocessed) CIFAR10 data.\n",
    "\n",
    "data = get_CIFAR10_data()\n",
    "for k, v in list(data.items()):\n",
    "  print(('%s: ' % k, v.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Affine layer: foward\n",
    "Open the file `cs231n/layers.py` and implement the `affine_forward` function.\n",
    "\n",
    "Once you are done you can test your implementaion by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing affine_forward function:\n",
      "difference:  9.76984946819e-10\n"
     ]
    }
   ],
   "source": [
    "# Test the affine_forward function\n",
    "\n",
    "num_inputs = 2\n",
    "input_shape = (4, 5, 6)\n",
    "output_dim = 3\n",
    "\n",
    "input_size = num_inputs * np.prod(input_shape)\n",
    "weight_size = output_dim * np.prod(input_shape)\n",
    "\n",
    "x = np.linspace(-0.1, 0.5, num=input_size).reshape(num_inputs, *input_shape)\n",
    "w = np.linspace(-0.2, 0.3, num=weight_size).reshape(np.prod(input_shape), output_dim)\n",
    "b = np.linspace(-0.3, 0.1, num=output_dim)\n",
    "\n",
    "out, _ = affine_forward(x, w, b)\n",
    "correct_out = np.array([[ 1.49834967,  1.70660132,  1.91485297],\n",
    "                        [ 3.25553199,  3.5141327,   3.77273342]])\n",
    "\n",
    "# Compare your output with ours. The error should be around 1e-9.\n",
    "print('Testing affine_forward function:')\n",
    "print('difference: ', rel_error(out, correct_out))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Affine layer: backward\n",
    "Now implement the `affine_backward` function and test your implementation using numeric gradient checking."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing affine_backward function:\n",
      "dx error:  5.39910036865e-11\n",
      "dw error:  9.9042118654e-11\n",
      "db error:  2.41228675681e-11\n"
     ]
    }
   ],
   "source": [
    "# Test the affine_backward function\n",
    "np.random.seed(231)\n",
    "x = np.random.randn(10, 2, 3)\n",
    "w = np.random.randn(6, 5)\n",
    "b = np.random.randn(5)\n",
    "dout = np.random.randn(10, 5)\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: affine_forward(x, w, b)[0], x, dout)\n",
    "dw_num = eval_numerical_gradient_array(lambda w: affine_forward(x, w, b)[0], w, dout)\n",
    "db_num = eval_numerical_gradient_array(lambda b: affine_forward(x, w, b)[0], b, dout)\n",
    "\n",
    "_, cache = affine_forward(x, w, b)\n",
    "dx, dw, db = affine_backward(dout, cache)\n",
    "\n",
    "# The error should be around 1e-10\n",
    "print('Testing affine_backward function:')\n",
    "print('dx error: ', rel_error(dx_num, dx))\n",
    "print('dw error: ', rel_error(dw_num, dw))\n",
    "print('db error: ', rel_error(db_num, db))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ReLU layer: forward\n",
    "Implement the forward pass for the ReLU activation function in the `relu_forward` function and test your implementation using the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing relu_forward function:\n",
      "difference:  4.99999979802e-08\n"
     ]
    }
   ],
   "source": [
    "# Test the relu_forward function\n",
    "\n",
    "x = np.linspace(-0.5, 0.5, num=12).reshape(3, 4)\n",
    "\n",
    "out, _ = relu_forward(x)\n",
    "correct_out = np.array([[ 0.,          0.,          0.,          0.,        ],\n",
    "                        [ 0.,          0.,          0.04545455,  0.13636364,],\n",
    "                        [ 0.22727273,  0.31818182,  0.40909091,  0.5,       ]])\n",
    "\n",
    "# Compare your output with ours. The error should be around 5e-8\n",
    "print('Testing relu_forward function:')\n",
    "print('difference: ', rel_error(out, correct_out))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ReLU layer: backward\n",
    "Now implement the backward pass for the ReLU activation function in the `relu_backward` function and test your implementation using numeric gradient checking:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing relu_backward function:\n",
      "dx error:  3.27563491363e-12\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "x = np.random.randn(10, 10)\n",
    "dout = np.random.randn(*x.shape)\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: relu_forward(x)[0], x, dout)\n",
    "\n",
    "_, cache = relu_forward(x)\n",
    "dx = relu_backward(dout, cache)\n",
    "\n",
    "# The error should be around 3e-12\n",
    "print('Testing relu_backward function:')\n",
    "print('dx error: ', rel_error(dx_num, dx))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# \"Sandwich\" layers\n",
    "There are some common patterns of layers that are frequently used in neural nets. For example, affine layers are frequently followed by a ReLU nonlinearity. To make these common patterns easy, we define several convenience layers in the file `cs231n/layer_utils.py`.\n",
    "\n",
    "For now take a look at the `affine_relu_forward` and `affine_relu_backward` functions, and run the following to numerically gradient check the backward pass:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing affine_relu_forward:\n",
      "dx error:  2.29957917731e-11\n",
      "dw error:  8.16201110576e-11\n",
      "db error:  7.82672402146e-12\n"
     ]
    }
   ],
   "source": [
    "from cs231n.layer_utils import affine_relu_forward, affine_relu_backward\n",
    "np.random.seed(231)\n",
    "x = np.random.randn(2, 3, 4)\n",
    "w = np.random.randn(12, 10)\n",
    "b = np.random.randn(10)\n",
    "dout = np.random.randn(2, 10)\n",
    "\n",
    "out, cache = affine_relu_forward(x, w, b)\n",
    "dx, dw, db = affine_relu_backward(dout, cache)\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: affine_relu_forward(x, w, b)[0], x, dout)\n",
    "dw_num = eval_numerical_gradient_array(lambda w: affine_relu_forward(x, w, b)[0], w, dout)\n",
    "db_num = eval_numerical_gradient_array(lambda b: affine_relu_forward(x, w, b)[0], b, dout)\n",
    "\n",
    "print('Testing affine_relu_forward:')\n",
    "print('dx error: ', rel_error(dx_num, dx))\n",
    "print('dw error: ', rel_error(dw_num, dw))\n",
    "print('db error: ', rel_error(db_num, db))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loss layers: Softmax and SVM\n",
    "You implemented these loss functions in the last assignment, so we'll give them to you for free here. You should still make sure you understand how they work by looking at the implementations in `cs231n/layers.py`.\n",
    "\n",
    "You can make sure that the implementations are correct by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing svm_loss:\n",
      "loss:  8.9996027491\n",
      "dx error:  1.40215660067e-09\n",
      "\n",
      "Testing softmax_loss:\n",
      "loss:  2.3025458445\n",
      "dx error:  9.38467316199e-09\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "num_classes, num_inputs = 10, 50\n",
    "x = 0.001 * np.random.randn(num_inputs, num_classes)\n",
    "y = np.random.randint(num_classes, size=num_inputs)\n",
    "\n",
    "dx_num = eval_numerical_gradient(lambda x: svm_loss(x, y)[0], x, verbose=False)\n",
    "loss, dx = svm_loss(x, y)\n",
    "\n",
    "# Test svm_loss function. Loss should be around 9 and dx error should be 1e-9\n",
    "print('Testing svm_loss:')\n",
    "print('loss: ', loss)\n",
    "print('dx error: ', rel_error(dx_num, dx))\n",
    "\n",
    "dx_num = eval_numerical_gradient(lambda x: softmax_loss(x, y)[0], x, verbose=False)\n",
    "loss, dx = softmax_loss(x, y)\n",
    "\n",
    "# Test softmax_loss function. Loss should be 2.3 and dx error should be 1e-8\n",
    "print('\\nTesting softmax_loss:')\n",
    "print('loss: ', loss)\n",
    "print('dx error: ', rel_error(dx_num, dx))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Two-layer network\n",
    "In the previous assignment you implemented a two-layer neural network in a single monolithic class. Now that you have implemented modular versions of the necessary layers, you will reimplement the two layer network using these modular implementations.\n",
    "\n",
    "Open the file `cs231n/classifiers/fc_net.py` and complete the implementation of the `TwoLayerNet` class. This class will serve as a model for the other networks you will implement in this assignment, so read through it to make sure you understand the API. You can run the cell below to test your implementation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing initialization ... \n",
      "Testing test-time forward pass ... \n",
      "Testing training loss (no regularization)\n",
      "Running numeric gradient check with reg =  0.0\n",
      "W1 relative error: 1.83e-08\n",
      "W2 relative error: 3.12e-10\n",
      "b1 relative error: 9.83e-09\n",
      "b2 relative error: 4.33e-10\n",
      "Running numeric gradient check with reg =  0.7\n",
      "W1 relative error: 2.53e-07\n",
      "W2 relative error: 2.85e-08\n",
      "b1 relative error: 1.56e-08\n",
      "b2 relative error: 7.76e-10\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "N, D, H, C = 3, 5, 50, 7\n",
    "X = np.random.randn(N, D)\n",
    "y = np.random.randint(C, size=N)\n",
    "\n",
    "std = 1e-3\n",
    "model = TwoLayerNet(input_dim=D, hidden_dim=H, num_classes=C, weight_scale=std)\n",
    "\n",
    "print('Testing initialization ... ')\n",
    "W1_std = abs(model.params['W1'].std() - std)\n",
    "b1 = model.params['b1']\n",
    "W2_std = abs(model.params['W2'].std() - std)\n",
    "b2 = model.params['b2']\n",
    "assert W1_std < std / 10, 'First layer weights do not seem right'\n",
    "assert np.all(b1 == 0), 'First layer biases do not seem right'\n",
    "assert W2_std < std / 10, 'Second layer weights do not seem right'\n",
    "assert np.all(b2 == 0), 'Second layer biases do not seem right'\n",
    "\n",
    "print('Testing test-time forward pass ... ')\n",
    "model.params['W1'] = np.linspace(-0.7, 0.3, num=D*H).reshape(D, H)\n",
    "model.params['b1'] = np.linspace(-0.1, 0.9, num=H)\n",
    "model.params['W2'] = np.linspace(-0.3, 0.4, num=H*C).reshape(H, C)\n",
    "model.params['b2'] = np.linspace(-0.9, 0.1, num=C)\n",
    "X = np.linspace(-5.5, 4.5, num=N*D).reshape(D, N).T\n",
    "scores = model.loss(X)\n",
    "correct_scores = np.asarray(\n",
    "  [[11.53165108,  12.2917344,   13.05181771,  13.81190102,  14.57198434, 15.33206765,  16.09215096],\n",
    "   [12.05769098,  12.74614105,  13.43459113,  14.1230412,   14.81149128, 15.49994135,  16.18839143],\n",
    "   [12.58373087,  13.20054771,  13.81736455,  14.43418138,  15.05099822, 15.66781506,  16.2846319 ]])\n",
    "scores_diff = np.abs(scores - correct_scores).sum()\n",
    "assert scores_diff < 1e-6, 'Problem with test-time forward pass'\n",
    "\n",
    "print('Testing training loss (no regularization)')\n",
    "y = np.asarray([0, 5, 1])\n",
    "loss, grads = model.loss(X, y)\n",
    "correct_loss = 3.4702243556\n",
    "assert abs(loss - correct_loss) < 1e-10, 'Problem with training-time loss'\n",
    "\n",
    "model.reg = 1.0\n",
    "loss, grads = model.loss(X, y)\n",
    "correct_loss = 26.5948426952\n",
    "assert abs(loss - correct_loss) < 1e-10, 'Problem with regularization loss'\n",
    "\n",
    "for reg in [0.0, 0.7]:\n",
    "  print('Running numeric gradient check with reg = ', reg)\n",
    "  model.reg = reg\n",
    "  loss, grads = model.loss(X, y)\n",
    "\n",
    "  for name in sorted(grads):\n",
    "    f = lambda _: model.loss(X, y)[0]\n",
    "    grad_num = eval_numerical_gradient(f, model.params[name], verbose=False)\n",
    "    print('%s relative error: %.2e' % (name, rel_error(grad_num, grads[name])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Solver\n",
    "In the previous assignment, the logic for training models was coupled to the models themselves. Following a more modular design, for this assignment we have split the logic for training models into a separate class.\n",
    "\n",
    "Open the file `cs231n/solver.py` and read through it to familiarize yourself with the API. After doing so, use a `Solver` instance to train a `TwoLayerNet` that achieves at least `50%` accuracy on the validation set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 4900) loss: 2.305632\n",
      "(Epoch 0 / 10) train acc: 0.086000; val_acc: 0.064000\n",
      "(Iteration 101 / 4900) loss: 2.301339\n",
      "(Iteration 201 / 4900) loss: 2.299413\n",
      "(Iteration 301 / 4900) loss: 2.298252\n",
      "(Iteration 401 / 4900) loss: 2.295628\n",
      "(Epoch 1 / 10) train acc: 0.202000; val_acc: 0.182000\n",
      "(Iteration 501 / 4900) loss: 2.289087\n",
      "(Iteration 601 / 4900) loss: 2.290871\n",
      "(Iteration 701 / 4900) loss: 2.291456\n",
      "(Iteration 801 / 4900) loss: 2.283665\n",
      "(Iteration 901 / 4900) loss: 2.276510\n",
      "(Epoch 2 / 10) train acc: 0.214000; val_acc: 0.210000\n",
      "(Iteration 1001 / 4900) loss: 2.275287\n",
      "(Iteration 1101 / 4900) loss: 2.288817\n",
      "(Iteration 1201 / 4900) loss: 2.274679\n",
      "(Iteration 1301 / 4900) loss: 2.242992\n",
      "(Iteration 1401 / 4900) loss: 2.252555\n",
      "(Epoch 3 / 10) train acc: 0.238000; val_acc: 0.217000\n",
      "(Iteration 1501 / 4900) loss: 2.262751\n",
      "(Iteration 1601 / 4900) loss: 2.250109\n",
      "(Iteration 1701 / 4900) loss: 2.220655\n",
      "(Iteration 1801 / 4900) loss: 2.213883\n",
      "(Iteration 1901 / 4900) loss: 2.220180\n",
      "(Epoch 4 / 10) train acc: 0.244000; val_acc: 0.217000\n",
      "(Iteration 2001 / 4900) loss: 2.225274\n",
      "(Iteration 2101 / 4900) loss: 2.141002\n",
      "(Iteration 2201 / 4900) loss: 2.204785\n",
      "(Iteration 2301 / 4900) loss: 2.188251\n",
      "(Iteration 2401 / 4900) loss: 2.185944\n",
      "(Epoch 5 / 10) train acc: 0.230000; val_acc: 0.217000\n",
      "(Iteration 2501 / 4900) loss: 2.210432\n",
      "(Iteration 2601 / 4900) loss: 2.168644\n",
      "(Iteration 2701 / 4900) loss: 2.164752\n",
      "(Iteration 2801 / 4900) loss: 2.027096\n",
      "(Iteration 2901 / 4900) loss: 2.128952\n",
      "(Epoch 6 / 10) train acc: 0.233000; val_acc: 0.241000\n",
      "(Iteration 3001 / 4900) loss: 2.168649\n",
      "(Iteration 3101 / 4900) loss: 2.184068\n",
      "(Iteration 3201 / 4900) loss: 2.097927\n",
      "(Iteration 3301 / 4900) loss: 2.166678\n",
      "(Iteration 3401 / 4900) loss: 2.170387\n",
      "(Epoch 7 / 10) train acc: 0.268000; val_acc: 0.260000\n",
      "(Iteration 3501 / 4900) loss: 2.085604\n",
      "(Iteration 3601 / 4900) loss: 2.087945\n",
      "(Iteration 3701 / 4900) loss: 2.084096\n",
      "(Iteration 3801 / 4900) loss: 2.065466\n",
      "(Iteration 3901 / 4900) loss: 2.090668\n",
      "(Epoch 8 / 10) train acc: 0.256000; val_acc: 0.280000\n",
      "(Iteration 4001 / 4900) loss: 2.206065\n",
      "(Iteration 4101 / 4900) loss: 2.144240\n",
      "(Iteration 4201 / 4900) loss: 2.006242\n",
      "(Iteration 4301 / 4900) loss: 2.016036\n",
      "(Iteration 4401 / 4900) loss: 2.076648\n",
      "(Epoch 9 / 10) train acc: 0.275000; val_acc: 0.282000\n",
      "(Iteration 4501 / 4900) loss: 1.992844\n",
      "(Iteration 4601 / 4900) loss: 2.123953\n",
      "(Iteration 4701 / 4900) loss: 2.076681\n",
      "(Iteration 4801 / 4900) loss: 2.007985\n",
      "(Epoch 10 / 10) train acc: 0.290000; val_acc: 0.289000\n"
     ]
    }
   ],
   "source": [
    "model = TwoLayerNet(reg=0.003)\n",
    "solver = None\n",
    "\n",
    "##############################################################################\n",
    "# TODO: Use a Solver instance to train a TwoLayerNet that achieves at least  #\n",
    "# 50% accuracy on the validation set.                                        #\n",
    "##############################################################################\n",
    "solver = Solver(model, data,\n",
    "                update_rule='sgd',\n",
    "                optim_config={\n",
    "                  'learning_rate': 1e-5,\n",
    "                },\n",
    "                lr_decay=0.95,\n",
    "                num_epochs=10, batch_size=100,\n",
    "                print_every=100)\n",
    "solver.train()\n",
    "##############################################################################\n",
    "#                             END OF YOUR CODE                               #\n",
    "##############################################################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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IznUCG4v3L+trmDCN7NkIJ9P4WVSkLwqm8RZL5QZhIz1/U+dGTexMzczzMRaE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Z6J2Ffkn7j3ZKUHPhY1cOmGPdKUqVIJAVFv/6B+efCY9nR1e4FYPijzf/7iYI/6BkbG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\nEYli/x88xKNNw5agEAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb2a6564358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Run this cell to visualize training loss and train / val accuracy\n",
    "\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.title('Training loss')\n",
    "plt.plot(solver.loss_history, 'o')\n",
    "plt.xlabel('Iteration')\n",
    "\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.title('Accuracy')\n",
    "plt.plot(solver.train_acc_history, '-o', label='train')\n",
    "plt.plot(solver.val_acc_history, '-o', label='val')\n",
    "plt.plot([0.5] * len(solver.val_acc_history), 'k--')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(loc='lower right')\n",
    "plt.gcf().set_size_inches(15, 12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multilayer network\n",
    "Next you will implement a fully-connected network with an arbitrary number of hidden layers.\n",
    "\n",
    "Read through the `FullyConnectedNet` class in the file `cs231n/classifiers/fc_net.py`.\n",
    "\n",
    "Implement the initialization, the forward pass, and the backward pass. For the moment don't worry about implementing dropout or batch normalization; we will add those features soon."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initial loss and gradient check"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As a sanity check, run the following to check the initial loss and to gradient check the network both with and without regularization. Do the initial losses seem reasonable?\n",
    "\n",
    "For gradient checking, you should expect to see errors around 1e-6 or less."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running check with reg =  0\n",
      "Initial loss:  2.30305969832\n",
      "W1 relative error: 3.04e-07\n",
      "W2 relative error: 9.48e-06\n",
      "W3 relative error: 1.14e-07\n",
      "b1 relative error: 1.19e-08\n",
      "b2 relative error: 1.90e-08\n",
      "b3 relative error: 1.45e-10\n",
      "Running check with reg =  3.14\n",
      "Initial loss:  7.05230905556\n",
      "W1 relative error: 4.95e-08\n",
      "W2 relative error: 3.52e-08\n",
      "W3 relative error: 8.66e-09\n",
      "b1 relative error: 3.25e-08\n",
      "b2 relative error: 3.96e-09\n",
      "b3 relative error: 2.41e-10\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "N, D, H1, H2, C = 2, 15, 20, 30, 10\n",
    "X = np.random.randn(N, D)\n",
    "y = np.random.randint(C, size=(N,))\n",
    "\n",
    "for reg in [0, 3.14]:\n",
    "  print('Running check with reg = ', reg)\n",
    "  model = FullyConnectedNet([H1, H2], input_dim=D, num_classes=C,\n",
    "                            reg=reg, weight_scale=5e-2, dtype=np.float64)\n",
    "\n",
    "  loss, grads = model.loss(X, y)\n",
    "  print('Initial loss: ', loss)\n",
    "\n",
    "  for name in sorted(grads):\n",
    "    f = lambda _: model.loss(X, y)[0]\n",
    "    grad_num = eval_numerical_gradient(f, model.params[name], verbose=False, h=1e-5)\n",
    "    print('%s relative error: %.2e' % (name, rel_error(grad_num, grads[name])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As another sanity check, make sure you can overfit a small dataset of 50 images. First we will try a three-layer network with 100 units in each hidden layer. You will need to tweak the learning rate and initialization scale, but you should be able to overfit and achieve 100% training accuracy within 20 epochs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 60) loss: 2.288205\n",
      "(Epoch 0 / 20) train acc: 0.220000; val_acc: 0.123000\n",
      "(Epoch 1 / 20) train acc: 0.260000; val_acc: 0.142000\n",
      "(Epoch 2 / 20) train acc: 0.260000; val_acc: 0.135000\n",
      "(Epoch 3 / 20) train acc: 0.400000; val_acc: 0.175000\n",
      "(Iteration 11 / 60) loss: 1.172877\n",
      "(Epoch 4 / 20) train acc: 0.520000; val_acc: 0.199000\n",
      "(Epoch 5 / 20) train acc: 0.620000; val_acc: 0.185000\n",
      "(Epoch 6 / 20) train acc: 0.700000; val_acc: 0.217000\n",
      "(Iteration 21 / 60) loss: 0.915680\n",
      "(Epoch 7 / 20) train acc: 0.660000; val_acc: 0.205000\n",
      "(Epoch 8 / 20) train acc: 0.720000; val_acc: 0.192000\n",
      "(Epoch 9 / 20) train acc: 0.740000; val_acc: 0.197000\n",
      "(Epoch 10 / 20) train acc: 0.760000; val_acc: 0.196000\n",
      "(Iteration 31 / 60) loss: 1.001700\n",
      "(Epoch 11 / 20) train acc: 0.760000; val_acc: 0.191000\n",
      "(Epoch 12 / 20) train acc: 0.780000; val_acc: 0.200000\n",
      "(Epoch 13 / 20) train acc: 0.780000; val_acc: 0.200000\n",
      "(Iteration 41 / 60) loss: 0.633953\n",
      "(Epoch 14 / 20) train acc: 0.760000; val_acc: 0.194000\n",
      "(Epoch 15 / 20) train acc: 0.760000; val_acc: 0.197000\n",
      "(Epoch 16 / 20) train acc: 0.760000; val_acc: 0.199000\n",
      "(Iteration 51 / 60) loss: 0.771941\n",
      "(Epoch 17 / 20) train acc: 0.780000; val_acc: 0.199000\n",
      "(Epoch 18 / 20) train acc: 0.780000; val_acc: 0.196000\n",
      "(Epoch 19 / 20) train acc: 0.780000; val_acc: 0.201000\n",
      "(Epoch 20 / 20) train acc: 0.780000; val_acc: 0.200000\n"
     ]
    },
    {
     "data": {
      "image/png": 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6NbdranxU+3bv1OhITZY0OlLTvt07mdAPABgIDFliS/o5t2tqfJQABqAttsdBWdFDhi3Z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\nBwSXgU0b8JXbAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb2a63d86d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TODO: Use a three-layer Net to overfit 50 training examples.\n",
    "\n",
    "num_train = 50\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "weight_scale = 1e-2\n",
    "learning_rate = 1e-2\n",
    "model = FullyConnectedNet([140, 100],\n",
    "              weight_scale=weight_scale, dtype=np.float64)\n",
    "solver = Solver(model, small_data,\n",
    "                print_every=10, num_epochs=20, batch_size=15,\n",
    "                update_rule='sgd',\n",
    "                optim_config={\n",
    "                  'learning_rate': learning_rate,\n",
    "                }\n",
    "         )\n",
    "solver.train()\n",
    "\n",
    "plt.plot(solver.loss_history, 'o')\n",
    "plt.title('Training loss history')\n",
    "plt.xlabel('Iteration')\n",
    "plt.ylabel('Training loss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now try to use a five-layer network with 100 units on each layer to overfit 50 training examples. Again you will have to adjust the learning rate and weight initialization, but you should be able to achieve 100% training accuracy within 20 epochs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 40) loss: 101.818138\n",
      "(Epoch 0 / 20) train acc: 0.080000; val_acc: 0.078000\n",
      "(Epoch 1 / 20) train acc: 0.120000; val_acc: 0.082000\n",
      "(Epoch 2 / 20) train acc: 0.160000; val_acc: 0.081000\n",
      "(Epoch 3 / 20) train acc: 0.220000; val_acc: 0.087000\n",
      "(Epoch 4 / 20) train acc: 0.280000; val_acc: 0.099000\n",
      "(Epoch 5 / 20) train acc: 0.300000; val_acc: 0.101000\n",
      "(Iteration 11 / 40) loss: 16.313613\n",
      "(Epoch 6 / 20) train acc: 0.360000; val_acc: 0.103000\n",
      "(Epoch 7 / 20) train acc: 0.360000; val_acc: 0.104000\n",
      "(Epoch 8 / 20) train acc: 0.380000; val_acc: 0.107000\n",
      "(Epoch 9 / 20) train acc: 0.460000; val_acc: 0.107000\n",
      "(Epoch 10 / 20) train acc: 0.500000; val_acc: 0.101000\n",
      "(Iteration 21 / 40) loss: 4.198234\n",
      "(Epoch 11 / 20) train acc: 0.540000; val_acc: 0.105000\n",
      "(Epoch 12 / 20) train acc: 0.540000; val_acc: 0.106000\n",
      "(Epoch 13 / 20) train acc: 0.540000; val_acc: 0.111000\n",
      "(Epoch 14 / 20) train acc: 0.580000; val_acc: 0.111000\n",
      "(Epoch 15 / 20) train acc: 0.580000; val_acc: 0.112000\n",
      "(Iteration 31 / 40) loss: 2.122295\n",
      "(Epoch 16 / 20) train acc: 0.580000; val_acc: 0.112000\n",
      "(Epoch 17 / 20) train acc: 0.580000; val_acc: 0.113000\n",
      "(Epoch 18 / 20) train acc: 0.580000; val_acc: 0.112000\n",
      "(Epoch 19 / 20) train acc: 0.580000; val_acc: 0.110000\n",
      "(Epoch 20 / 20) train acc: 0.580000; val_acc: 0.112000\n"
     ]
    },
    {
     "data": {
      "image/png": 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ap7w5bMuumTnO3HE5J5//Mc7ccTm7ZuaaLkmSjokTAqR1ypvDOilCUn8ynEnr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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb2a63fd6a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TODO: Use a five-layer Net to overfit 50 training examples.\n",
    "\n",
    "num_train = 50\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "learning_rate = 0.0001\n",
    "weight_scale = 0.1\n",
    "model = FullyConnectedNet([100, 100, 100, 100],\n",
    "                weight_scale=weight_scale, dtype=np.float64)\n",
    "solver = Solver(model, small_data,\n",
    "                print_every=10, num_epochs=20, batch_size=25,\n",
    "                update_rule='sgd',\n",
    "                optim_config={\n",
    "                  'learning_rate': learning_rate,\n",
    "                }\n",
    "         )\n",
    "solver.train()\n",
    "\n",
    "plt.plot(solver.loss_history, 'o')\n",
    "plt.title('Training loss history')\n",
    "plt.xlabel('Iteration')\n",
    "plt.ylabel('Training loss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Inline question: \n",
    "Did you notice anything about the comparative difficulty of training the three-layer net vs training the five layer net?\n",
    "\n",
    "# Answer:\n",
    "[FILL THIS IN]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Update rules\n",
    "So far we have used vanilla stochastic gradient descent (SGD) as our update rule. More sophisticated update rules can make it easier to train deep networks. We will implement a few of the most commonly used update rules and compare them to vanilla SGD."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SGD+Momentum\n",
    "Stochastic gradient descent with momentum is a widely used update rule that tends to make deep networks converge faster than vanilla stochstic gradient descent.\n",
    "\n",
    "Open the file `cs231n/optim.py` and read the documentation at the top of the file to make sure you understand the API. Implement the SGD+momentum update rule in the function `sgd_momentum` and run the following to check your implementation. You should see errors less than 1e-8."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "next_w error:  8.88234703351e-09\n",
      "velocity error:  4.26928774328e-09\n"
     ]
    }
   ],
   "source": [
    "from cs231n.optim import sgd_momentum\n",
    "\n",
    "N, D = 4, 5\n",
    "w = np.linspace(-0.4, 0.6, num=N*D).reshape(N, D)\n",
    "dw = np.linspace(-0.6, 0.4, num=N*D).reshape(N, D)\n",
    "v = np.linspace(0.6, 0.9, num=N*D).reshape(N, D)\n",
    "\n",
    "config = {'learning_rate': 1e-3, 'velocity': v}\n",
    "next_w, _ = sgd_momentum(w, dw, config=config)\n",
    "\n",
    "expected_next_w = np.asarray([\n",
    "  [ 0.1406,      0.20738947,  0.27417895,  0.34096842,  0.40775789],\n",
    "  [ 0.47454737,  0.54133684,  0.60812632,  0.67491579,  0.74170526],\n",
    "  [ 0.80849474,  0.87528421,  0.94207368,  1.00886316,  1.07565263],\n",
    "  [ 1.14244211,  1.20923158,  1.27602105,  1.34281053,  1.4096    ]])\n",
    "expected_velocity = np.asarray([\n",
    "  [ 0.5406,      0.55475789,  0.56891579, 0.58307368,  0.59723158],\n",
    "  [ 0.61138947,  0.62554737,  0.63970526,  0.65386316,  0.66802105],\n",
    "  [ 0.68217895,  0.69633684,  0.71049474,  0.72465263,  0.73881053],\n",
    "  [ 0.75296842,  0.76712632,  0.78128421,  0.79544211,  0.8096    ]])\n",
    "\n",
    "print('next_w error: ', rel_error(next_w, expected_next_w))\n",
    "print('velocity error: ', rel_error(expected_velocity, config['velocity']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once you have done so, run the following to train a six-layer network with both SGD and SGD+momentum. You should see the SGD+momentum update rule converge faster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running with  sgd\n",
      "(Iteration 1 / 200) loss: 2.385464\n",
      "(Epoch 0 / 5) train acc: 0.103000; val_acc: 0.120000\n",
      "(Iteration 11 / 200) loss: 2.335066\n",
      "(Iteration 21 / 200) loss: 2.327772\n",
      "(Iteration 31 / 200) loss: 2.316712\n",
      "(Epoch 1 / 5) train acc: 0.110000; val_acc: 0.131000\n",
      "(Iteration 41 / 200) loss: 2.332864\n",
      "(Iteration 51 / 200) loss: 2.301143\n",
      "(Iteration 61 / 200) loss: 2.316728\n",
      "(Iteration 71 / 200) loss: 2.302126\n",
      "(Epoch 2 / 5) train acc: 0.112000; val_acc: 0.145000\n",
      "(Iteration 81 / 200) loss: 2.271681\n",
      "(Iteration 91 / 200) loss: 2.303540\n",
      "(Iteration 101 / 200) loss: 2.293706\n",
      "(Iteration 111 / 200) loss: 2.304197\n",
      "(Epoch 3 / 5) train acc: 0.127000; val_acc: 0.146000\n",
      "(Iteration 121 / 200) loss: 2.284977\n",
      "(Iteration 131 / 200) loss: 2.264977\n",
      "(Iteration 141 / 200) loss: 2.286909\n",
      "(Iteration 151 / 200) loss: 2.277968\n",
      "(Epoch 4 / 5) train acc: 0.157000; val_acc: 0.156000\n",
      "(Iteration 161 / 200) loss: 2.293132\n",
      "(Iteration 171 / 200) loss: 2.297544\n",
      "(Iteration 181 / 200) loss: 2.300910\n",
      "(Iteration 191 / 200) loss: 2.316477\n",
      "(Epoch 5 / 5) train acc: 0.125000; val_acc: 0.159000\n",
      "\n",
      "running with  sgd_momentum\n",
      "(Iteration 1 / 200) loss: 2.666328\n",
      "(Epoch 0 / 5) train acc: 0.105000; val_acc: 0.069000\n",
      "(Iteration 11 / 200) loss: 2.373000\n",
      "(Iteration 21 / 200) loss: 2.299668\n",
      "(Iteration 31 / 200) loss: 2.290291\n",
      "(Epoch 1 / 5) train acc: 0.113000; val_acc: 0.110000\n",
      "(Iteration 41 / 200) loss: 2.300137\n",
      "(Iteration 51 / 200) loss: 2.296630\n",
      "(Iteration 61 / 200) loss: 2.274112\n",
      "(Iteration 71 / 200) loss: 2.254658\n",
      "(Epoch 2 / 5) train acc: 0.129000; val_acc: 0.133000\n",
      "(Iteration 81 / 200) loss: 2.240168\n",
      "(Iteration 91 / 200) loss: 2.269001\n",
      "(Iteration 101 / 200) loss: 2.178781\n",
      "(Iteration 111 / 200) loss: 2.271836\n",
      "(Epoch 3 / 5) train acc: 0.205000; val_acc: 0.192000\n",
      "(Iteration 121 / 200) loss: 2.246839\n",
      "(Iteration 131 / 200) loss: 2.096185\n",
      "(Iteration 141 / 200) loss: 2.136125\n",
      "(Iteration 151 / 200) loss: 2.195513\n",
      "(Epoch 4 / 5) train acc: 0.230000; val_acc: 0.234000\n",
      "(Iteration 161 / 200) loss: 2.141696\n",
      "(Iteration 171 / 200) loss: 2.191472\n",
      "(Iteration 181 / 200) loss: 2.247404\n",
      "(Iteration 191 / 200) loss: 2.151423\n",
      "(Epoch 5 / 5) train acc: 0.253000; val_acc: 0.253000\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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IoYbKQkREREREVFeM9Oip6nZVXV/4+Q0AmwGMs232GQArVfXvhe1eMVEWIiIiIiKiemN8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\niEiWUdATERERERHJMv8f4QBK5yfrRUoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb2a4a77630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_train = 4000\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "solvers = {}\n",
    "\n",
    "for update_rule in ['sgd', 'sgd_momentum']:\n",
    "  print('running with ', update_rule)\n",
    "  model = FullyConnectedNet([100, 100, 100, 100, 100], weight_scale=5e-2)\n",
    "\n",
    "  solver = Solver(model, small_data,\n",
    "                  num_epochs=5, batch_size=100,\n",
    "                  update_rule=update_rule,\n",
    "                  optim_config={\n",
    "                    'learning_rate': 5e-4,\n",
    "                  },\n",
    "                  verbose=True)\n",
    "  solvers[update_rule] = solver\n",
    "  solver.train()\n",
    "  print()\n",
    "\n",
    "plt.subplot(3, 1, 1)\n",
    "plt.title('Training loss')\n",
    "plt.xlabel('Iteration')\n",
    "\n",
    "plt.subplot(3, 1, 2)\n",
    "plt.title('Training accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "plt.subplot(3, 1, 3)\n",
    "plt.title('Validation accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "for update_rule, solver in list(solvers.items()):\n",
    "  plt.subplot(3, 1, 1)\n",
    "  plt.plot(solver.loss_history, 'o', label=update_rule)\n",
    "  \n",
    "  plt.subplot(3, 1, 2)\n",
    "  plt.plot(solver.train_acc_history, '-o', label=update_rule)\n",
    "\n",
    "  plt.subplot(3, 1, 3)\n",
    "  plt.plot(solver.val_acc_history, '-o', label=update_rule)\n",
    "  \n",
    "for i in [1, 2, 3]:\n",
    "  plt.subplot(3, 1, i)\n",
    "  plt.legend(loc='upper center', ncol=4)\n",
    "plt.gcf().set_size_inches(15, 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# RMSProp and Adam\n",
    "RMSProp [1] and Adam [2] are update rules that set per-parameter learning rates by using a running average of the second moments of gradients.\n",
    "\n",
    "In the file `cs231n/optim.py`, implement the RMSProp update rule in the `rmsprop` function and implement the Adam update rule in the `adam` function, and check your implementations using the tests below.\n",
    "\n",
    "[1] Tijmen Tieleman and Geoffrey Hinton. \"Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude.\" COURSERA: Neural Networks for Machine Learning 4 (2012).\n",
    "\n",
    "[2] Diederik Kingma and Jimmy Ba, \"Adam: A Method for Stochastic Optimization\", ICLR 2015."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "next_w error:  9.52468751104e-08\n",
      "cache error:  2.64779558072e-09\n"
     ]
    }
   ],
   "source": [
    "# Test RMSProp implementation; you should see errors less than 1e-7\n",
    "from cs231n.optim import rmsprop\n",
    "\n",
    "N, D = 4, 5\n",
    "w = np.linspace(-0.4, 0.6, num=N*D).reshape(N, D)\n",
    "dw = np.linspace(-0.6, 0.4, num=N*D).reshape(N, D)\n",
    "cache = np.linspace(0.6, 0.9, num=N*D).reshape(N, D)\n",
    "\n",
    "config = {'learning_rate': 1e-2, 'cache': cache}\n",
    "next_w, _ = rmsprop(w, dw, config=config)\n",
    "\n",
    "expected_next_w = np.asarray([\n",
    "  [-0.39223849, -0.34037513, -0.28849239, -0.23659121, -0.18467247],\n",
    "  [-0.132737,   -0.08078555, -0.02881884,  0.02316247,  0.07515774],\n",
    "  [ 0.12716641,  0.17918792,  0.23122175,  0.28326742,  0.33532447],\n",
    "  [ 0.38739248,  0.43947102,  0.49155973,  0.54365823,  0.59576619]])\n",
    "expected_cache = np.asarray([\n",
    "  [ 0.5976,      0.6126277,   0.6277108,   0.64284931,  0.65804321],\n",
    "  [ 0.67329252,  0.68859723,  0.70395734,  0.71937285,  0.73484377],\n",
    "  [ 0.75037008,  0.7659518,   0.78158892,  0.79728144,  0.81302936],\n",
    "  [ 0.82883269,  0.84469141,  0.86060554,  0.87657507,  0.8926    ]])\n",
    "\n",
    "print('next_w error: ', rel_error(expected_next_w, next_w))\n",
    "print('cache error: ', rel_error(expected_cache, config['cache']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "next_w error:  1.13956917985e-07\n",
      "v error:  4.20831403811e-09\n",
      "m error:  4.21496319311e-09\n"
     ]
    }
   ],
   "source": [
    "# Test Adam implementation; you should see errors around 1e-7 or less\n",
    "from cs231n.optim import adam\n",
    "\n",
    "N, D = 4, 5\n",
    "w = np.linspace(-0.4, 0.6, num=N*D).reshape(N, D)\n",
    "dw = np.linspace(-0.6, 0.4, num=N*D).reshape(N, D)\n",
    "m = np.linspace(0.6, 0.9, num=N*D).reshape(N, D)\n",
    "v = np.linspace(0.7, 0.5, num=N*D).reshape(N, D)\n",
    "\n",
    "config = {'learning_rate': 1e-2, 'm': m, 'v': v, 't': 5}\n",
    "next_w, _ = adam(w, dw, config=config)\n",
    "\n",
    "expected_next_w = np.asarray([\n",
    "  [-0.40094747, -0.34836187, -0.29577703, -0.24319299, -0.19060977],\n",
    "  [-0.1380274,  -0.08544591, -0.03286534,  0.01971428,  0.0722929],\n",
    "  [ 0.1248705,   0.17744702,  0.23002243,  0.28259667,  0.33516969],\n",
    "  [ 0.38774145,  0.44031188,  0.49288093,  0.54544852,  0.59801459]])\n",
    "expected_v = np.asarray([\n",
    "  [ 0.69966,     0.68908382,  0.67851319,  0.66794809,  0.65738853,],\n",
    "  [ 0.64683452,  0.63628604,  0.6257431,   0.61520571,  0.60467385,],\n",
    "  [ 0.59414753,  0.58362676,  0.57311152,  0.56260183,  0.55209767,],\n",
    "  [ 0.54159906,  0.53110598,  0.52061845,  0.51013645,  0.49966,   ]])\n",
    "expected_m = np.asarray([\n",
    "  [ 0.48,        0.49947368,  0.51894737,  0.53842105,  0.55789474],\n",
    "  [ 0.57736842,  0.59684211,  0.61631579,  0.63578947,  0.65526316],\n",
    "  [ 0.67473684,  0.69421053,  0.71368421,  0.73315789,  0.75263158],\n",
    "  [ 0.77210526,  0.79157895,  0.81105263,  0.83052632,  0.85      ]])\n",
    "\n",
    "print('next_w error: ', rel_error(expected_next_w, next_w))\n",
    "print('v error: ', rel_error(expected_v, config['v']))\n",
    "print('m error: ', rel_error(expected_m, config['m']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once you have debugged your RMSProp and Adam implementations, run the following to train a pair of deep networks using these new update rules:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running with  adam\n",
      "(Iteration 1 / 200) loss: 2.558531\n",
      "(Epoch 0 / 5) train acc: 0.136000; val_acc: 0.106000\n",
      "(Iteration 11 / 200) loss: 2.077011\n",
      "(Iteration 21 / 200) loss: 2.087202\n",
      "(Iteration 31 / 200) loss: 2.031286\n",
      "(Epoch 1 / 5) train acc: 0.310000; val_acc: 0.294000\n",
      "(Iteration 41 / 200) loss: 2.124100\n",
      "(Iteration 51 / 200) loss: 1.852028\n",
      "(Iteration 61 / 200) loss: 1.815337\n",
      "(Iteration 71 / 200) loss: 2.053566\n",
      "(Epoch 2 / 5) train acc: 0.361000; val_acc: 0.327000\n",
      "(Iteration 81 / 200) loss: 2.036893\n",
      "(Iteration 91 / 200) loss: 1.884802\n",
      "(Iteration 101 / 200) loss: 1.992917\n",
      "(Iteration 111 / 200) loss: 1.882585\n",
      "(Epoch 3 / 5) train acc: 0.401000; val_acc: 0.324000\n",
      "(Iteration 121 / 200) loss: 1.714800\n",
      "(Iteration 131 / 200) loss: 1.890103\n",
      "(Iteration 141 / 200) loss: 1.851711\n",
      "(Iteration 151 / 200) loss: 1.691300\n",
      "(Epoch 4 / 5) train acc: 0.443000; val_acc: 0.333000\n",
      "(Iteration 161 / 200) loss: 1.644283\n",
      "(Iteration 171 / 200) loss: 1.782927\n",
      "(Iteration 181 / 200) loss: 1.625438\n",
      "(Iteration 191 / 200) loss: 1.635718\n",
      "(Epoch 5 / 5) train acc: 0.484000; val_acc: 0.358000\n",
      "\n",
      "running with  rmsprop\n",
      "(Iteration 1 / 200) loss: 2.398701\n",
      "(Epoch 0 / 5) train acc: 0.107000; val_acc: 0.086000\n",
      "(Iteration 11 / 200) loss: 2.203484\n",
      "(Iteration 21 / 200) loss: 2.039575\n",
      "(Iteration 31 / 200) loss: 2.057661\n",
      "(Epoch 1 / 5) train acc: 0.308000; val_acc: 0.272000\n",
      "(Iteration 41 / 200) loss: 2.060504\n",
      "(Iteration 51 / 200) loss: 2.053137\n",
      "(Iteration 61 / 200) loss: 1.895695\n",
      "(Iteration 71 / 200) loss: 1.991557\n",
      "(Epoch 2 / 5) train acc: 0.369000; val_acc: 0.271000\n",
      "(Iteration 81 / 200) loss: 1.807487\n",
      "(Iteration 91 / 200) loss: 1.960083\n",
      "(Iteration 101 / 200) loss: 1.768650\n",
      "(Iteration 111 / 200) loss: 1.886395\n",
      "(Epoch 3 / 5) train acc: 0.394000; val_acc: 0.305000\n",
      "(Iteration 121 / 200) loss: 1.797069\n",
      "(Iteration 131 / 200) loss: 1.729815\n",
      "(Iteration 141 / 200) loss: 1.766147\n",
      "(Iteration 151 / 200) loss: 1.613428\n",
      "(Epoch 4 / 5) train acc: 0.433000; val_acc: 0.321000\n",
      "(Iteration 161 / 200) loss: 1.619454\n",
      "(Iteration 171 / 200) loss: 1.892600\n",
      "(Iteration 181 / 200) loss: 1.707247\n",
      "(Iteration 191 / 200) loss: 1.664216\n",
      "(Epoch 5 / 5) train acc: 0.460000; val_acc: 0.316000\n",
      "\n"
     ]
    },
    {
     "data": {
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JE7Bu3TqceeaZNp2ZIA48WzItbKX73LBhAwYPHoyBAwca2vbcc8/FoUOHEhPiYDCY5P6n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eZnjKpcikhWrV69OlR17duLVvHWxpaYy/59/Q7aOjq7PF5RWsKRowd6vubQ+mpumTMx5ns++uij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\nLareNuccn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yAiIkcbX8DHzqad/ZqRFDcU09jR3Y7f5XCRG5/bHdhiJpAfSCK7PYbYxg4C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6fA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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb2a48b1128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learning_rates = {'rmsprop': 1e-4, 'adam': 1e-3}\n",
    "for update_rule in ['adam', 'rmsprop']:\n",
    "  print('running with ', update_rule)\n",
    "  model = FullyConnectedNet([100, 100, 100, 100, 100], weight_scale=5e-2)\n",
    "\n",
    "  solver = Solver(model, small_data,\n",
    "                  num_epochs=5, batch_size=100,\n",
    "                  update_rule=update_rule,\n",
    "                  optim_config={\n",
    "                    'learning_rate': learning_rates[update_rule]\n",
    "                  },\n",
    "                  verbose=True)\n",
    "  solvers[update_rule] = solver\n",
    "  solver.train()\n",
    "  print()\n",
    "\n",
    "plt.subplot(3, 1, 1)\n",
    "plt.title('Training loss')\n",
    "plt.xlabel('Iteration')\n",
    "\n",
    "plt.subplot(3, 1, 2)\n",
    "plt.title('Training accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "plt.subplot(3, 1, 3)\n",
    "plt.title('Validation accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "for update_rule, solver in list(solvers.items()):\n",
    "  plt.subplot(3, 1, 1)\n",
    "  plt.plot(solver.loss_history, 'o', label=update_rule)\n",
    "  \n",
    "  plt.subplot(3, 1, 2)\n",
    "  plt.plot(solver.train_acc_history, '-o', label=update_rule)\n",
    "\n",
    "  plt.subplot(3, 1, 3)\n",
    "  plt.plot(solver.val_acc_history, '-o', label=update_rule)\n",
    "  \n",
    "for i in [1, 2, 3]:\n",
    "  plt.subplot(3, 1, i)\n",
    "  plt.legend(loc='upper center', ncol=4)\n",
    "plt.gcf().set_size_inches(15, 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train a good model!\n",
    "Train the best fully-connected model that you can on CIFAR-10, storing your best model in the `best_model` variable. We require you to get at least 50% accuracy on the validation set using a fully-connected net.\n",
    "\n",
    "If you are careful it should be possible to get accuracies above 55%, but we don't require it for this part and won't assign extra credit for doing so. Later in the assignment we will ask you to train the best convolutional network that you can on CIFAR-10, and we would prefer that you spend your effort working on convolutional nets rather than fully-connected nets.\n",
    "\n",
    "You might find it useful to complete the `BatchNormalization.ipynb` and `Dropout.ipynb` notebooks before completing this part, since those techniques can help you train powerful models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 4000) loss: 2.353428\n",
      "(Epoch 0 / 200) train acc: 0.098000; val_acc: 0.100000\n",
      "(Epoch 1 / 200) train acc: 0.100000; val_acc: 0.098000\n",
      "(Epoch 2 / 200) train acc: 0.105000; val_acc: 0.096000\n",
      "(Epoch 3 / 200) train acc: 0.111000; val_acc: 0.101000\n",
      "(Epoch 4 / 200) train acc: 0.109000; val_acc: 0.102000\n",
      "(Epoch 5 / 200) train acc: 0.085000; val_acc: 0.105000\n",
      "(Epoch 6 / 200) train acc: 0.108000; val_acc: 0.099000\n",
      "(Epoch 7 / 200) train acc: 0.093000; val_acc: 0.099000\n",
      "(Epoch 8 / 200) train acc: 0.112000; val_acc: 0.109000\n",
      "(Epoch 9 / 200) train acc: 0.137000; val_acc: 0.125000\n",
      "(Epoch 10 / 200) train acc: 0.124000; val_acc: 0.125000\n",
      "(Epoch 11 / 200) train acc: 0.143000; val_acc: 0.128000\n",
      "(Epoch 12 / 200) train acc: 0.149000; val_acc: 0.151000\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-45-4577c7b1fd84>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     26\u001b[0m                                 print_every=1000)\n\u001b[1;32m     27\u001b[0m                 \u001b[0msolvers\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mupdate_rule\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msolver\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 28\u001b[0;31m                 \u001b[0msolver\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     29\u001b[0m                 \u001b[0mval_acc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msolver\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbest_val_acc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     30\u001b[0m                 print(\"learning_rate:%.10lf,batch_size:%d,weight_scale:%.10lf.\"\n",
      "\u001b[0;32m/home/wjb/cs231n/assignment/assignment2/cs231n/solver.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    264\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    265\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mt\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum_iterations\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 266\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_step\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    267\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    268\u001b[0m             \u001b[0;31m# Maybe print training loss\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/wjb/cs231n/assignment/assignment2/cs231n/solver.py\u001b[0m in \u001b[0;36m_step\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    180\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    181\u001b[0m         \u001b[0;31m# Compute loss and gradient\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 182\u001b[0;31m         \u001b[0mloss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrads\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_batch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_batch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    183\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloss_history\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    184\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/wjb/cs231n/assignment/assignment2/cs231n/classifiers/fc_net.py\u001b[0m in \u001b[0;36mloss\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m    338\u001b[0m             \u001b[0;31m#backforward\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    339\u001b[0m             \u001b[0mdxi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdWi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdbi\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maffine_backward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfc_cache_list\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mlayer\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 340\u001b[0;31m             \u001b[0mdWi\u001b[0m\u001b[0;34m+=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreg\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'W%d'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mlayer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    341\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    342\u001b[0m             \u001b[0mgrads\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'W%d'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mlayer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdWi\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "best_model = None\n",
    "################################################################################\n",
    "# TODO: Train the best FullyConnectedNet that you can on CIFAR-10. You might   #\n",
    "# batch normalization and dropout useful. Store your best model in the         #\n",
    "# best_model variable.                                                         #\n",
    "################################################################################\n",
    "learning_rates = [1e-3]\n",
    "weight_scale_choices = [5e-2]\n",
    "batch_size_choices=[200]\n",
    "best_accuracy=0\n",
    "#[163, 157, 65,100, 80]\n",
    "\n",
    "for learning_ratei in learning_rates:\n",
    "    for wei in weight_scale_choices:\n",
    "        for bi in batch_size_choices:\n",
    "                model = FullyConnectedNet([100, 100, 100,100, 100], \n",
    "                                          weight_scale=wei,reg=0.0001,\n",
    "                                         dropout=0.6,use_batchnorm=True)\n",
    "                solver = Solver(model, small_data,\n",
    "                                  num_epochs=200, batch_size=bi,\n",
    "                                  update_rule='adam',\n",
    "                                  optim_config={\n",
    "                                    'learning_rate': learning_ratei\n",
    "                                  },\n",
    "                                  verbose=True,\n",
    "                                print_every=1000)\n",
    "                solvers[update_rule] = solver\n",
    "                solver.train()\n",
    "                val_acc = solver.best_val_acc\n",
    "                print(\"learning_rate:%.10lf,batch_size:%d,weight_scale:%.10lf.\"\n",
    "                      % (learning_ratei, bi, wei))\n",
    "                print(\"val_acc:%.10lf.\\n\"% val_acc)\n",
    "                if val_acc > best_accuracy:\n",
    "                    best_accuracy = val_acc\n",
    "                    best_model = model\n",
    "                \n",
    "print(\"best_val_acc:%.10f\"%best_accuracy)\n",
    "################################################################################\n",
    "#                              END OF YOUR CODE                                #\n",
    "################################################################################"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test you model\n",
    "Run your best model on the validation and test sets. You should achieve above 50% accuracy on the validation set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation set accuracy:  0.375\n",
      "Test set accuracy:  0.336\n"
     ]
    }
   ],
   "source": [
    "y_test_pred = np.argmax(best_model.loss(data['X_test']), axis=1)\n",
    "y_val_pred = np.argmax(best_model.loss(data['X_val']), axis=1)\n",
    "print('Validation set accuracy: ', (y_val_pred == data['y_val']).mean())\n",
    "print('Test set accuracy: ', (y_test_pred == data['y_test']).mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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    "collapsed": true
   },
   "outputs": [],
   "source": []
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